Isomorphism analysis on generalized modules oriented to the distributed parameterized intelligent product platform

doi:10.1007/s11465-019-0555-9

Front. Mech. Eng.

2020, Vol. 15

Issue (1) : 12-23 https://doi.org/10.1007/s11465-019-0555-9

RESEARCH ARTICLE

Isomorphism analysis on generalized modules oriented to the distributed parameterized intelligent product platform

Shasha ZENG(

), Weiping PENG, Tiaoyu LEI

Hubei Key Laboratory of Waterjet Theory and New Technology, School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China

Download: PDF(606 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

The distributed parameterized intelligent product platform (DPIPP) contains many agents of a product minimum approximate autonomous subsystem (generalized module). These distributed agents communicate, coordinate, and cooperate using their knowledge and skills and eventually accomplish the design for mass customization in a loosely coupled environment. In this study, a new method of isomorphism analysis on generalized modules oriented to DPIPP is proposed. First, on the basis of the bill of material partition and generalized module mining, the parameters of the main characteristics are extracted to construct the main characteristic parameter matrix. Second, similarity calculation of generalized modules is realized by improving the clustering using representatives algorithm, and isomorphism model sets are obtained. Generalized modules with a similar structure are combined to complete the isomorphism analysis. The effectiveness of the proposed method is verified by taking high- and medium-pressure valve data as an example.

Keywords distributed parameterized intelligent product platform generalized module isomorphism analysis product family

Corresponding Author(s): Shasha ZENG

Just Accepted Date: 24 October 2019 Online First Date: 11 December 2019 Issue Date: 21 February 2020

Cite this article:

Shasha ZENG,Weiping PENG,Tiaoyu LEI. Isomorphism analysis on generalized modules oriented to the distributed parameterized intelligent product platform[J]. Front. Mech. Eng., 2020, 15(1): 12-23.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-019-0555-9
https://academic.hep.com.cn/fme/EN/Y2020/V15/I1/12

Fig.1 Schematic of the DPIPP model. SA^k: Isomorphic class model; SF^q: Equivalent class model; SP^p: Equivalence class model; →: Inclusion relation; PD: Product dataset.

Fig.2 Tree organizational model of DPIPP. BOM: Bill of materials.

Fig.3 Two types of models in the non-isomorphism class. SA^k: The set of isomorphism class model; M_ij: Generalized module; SF: The set of functional equivalent class; SP: The set of process equivalence class.

Fig.4 Matching relationship between parameters.

Fig.5 Flow chart of the cohesive hierarchical clustering algorithm.

Fig.6 Clustering results of the generalized module.

Tab.1 Experimental data of the valve module

Fig.7 Algorithm flowchart of the isomorphism analysis of generalized modules.

Fig.8 Clustering spectrum of the valve module.

1	X Shang, Z Shen, G Xiong, et al. Moving from mass customization to social manufacturing: A footwear industry case study. International Journal of Computer Integrated Manufacturing, 2019, 32(2): 194–205 https://doi.org/10.1080/0951192X.2018.1550675
2	M Zhang, H Guo, B Huo, et al. Linking supply chain quality integration with mass customization and product modularity. International Journal of Production Economics, 2019, 207: 227–235 https://doi.org/10.1016/j.ijpe.2017.01.011
3	T Hara, T Sakao, R Fukushima. Customization of product, service, and product/service system: What and how to design. Mechanical Engineering Reviews, 2019, 6(1): 18-00184 https://doi.org/10.1299/mer.18-00184
4	P Veloso, G Celani, R Scheeren. From the generation of layouts to the production of construction documents: An application in the customization of apartment plans. Automation in Construction, 2018, 96: 224–235 https://doi.org/10.1016/j.autcon.2018.09.013
5	D Deradjat, T Minshall. Decision trees for implementing rapid manufacturing for mass customisation. CIRP Journal of Manufacturing Science and Technology, 2018, 23: 156–171 https://doi.org/10.1016/j.cirpj.2017.12.003
6	B B Fan, G Qi, X Hu, et al. A network methodology for structure-oriented modular product platform planning. Journal of Intelligent Manufacturing, 2015, 26(3): 553–570 https://doi.org/10.1007/s10845-013-0815-1
7	F Zhou, Y Ji, R J Jiao. Affective and cognitive design for mass personalization: Status and prospect. Journal of Intelligent Manufacturing, 2013, 24(5): 1047–1069 https://doi.org/10.1007/s10845-012-0673-2
8	J Jiao, M M Tseng. A methodology of developing product family architecture for mass customization. Journal of Intelligent Manufacturing, 1999, 10(1): 3–20 https://doi.org/10.1023/A:1008926428533
9	Y Xiong, G Du, R J Jiao. Modular product platforming with supply chain postponement decisions by leader-follower interactive optimization. International Journal of Production Economics, 2018, 205: 272–286 https://doi.org/10.1016/j.ijpe.2018.09.013
10	M T Hayasi, B Asiabanpour. Extraction of manufacturing information from design-by-feature solid model through feature recognition. International Journal of Advanced Manufacturing Technology, 2009, 44(11‒12): 1191–1203 https://doi.org/10.1007/s00170-008-1922-6
11	A S Freire, R M Cesar, C E Ferreira. A column generation approach for the graph matching problem. In: Proceedings of the 2010 20th International Conference on Pattern Recognition. Istanbul: IEEE, 2010, 1088–1091 https://doi.org/10.1109/ICPR.2010.272
12	Y Narabu, J Zhu, T Tanaka, et al. Automatic manufacturing feature extraction of CAD models for machining. Key Engineering Materials, 2010, 447–448: 287–291 https://doi.org/10.4028/www.scientific.net/KEM.447-448.287
13	B Venu, V R Komma, D Srivastava. STEP-based feature recognition system for B-spline surface features. International Journal of Automation and Computing, 2018, 15(4): 500–512 https://doi.org/10.1007/s11633-018-1116-0
14	J Liu, X Liu, Y Cheng, et al. An approach to mapping machining feature to manufacturing feature volume based on geometric reasoning for process planning. Proceedings of the Institution of Mechanical Engineers. Part B, Journal of Engineering Manufacture, 2017, 231(7): 1204–1216 https://doi.org/10.1177/0954405415585377
15	B Yi, X Li, Y Yang. Heterogeneous model integration of complex mechanical parts based on semantic feature fusion. Engineering with Computers, 2017, 33(4): 797–805 https://doi.org/10.1007/s00366-016-0498-2
16	V B Sunil, S S Pande. Automatic recognition of features from freeform surface CAD models. Computer Aided Design, 2008, 40(4): 502–517 https://doi.org/10.1016/j.cad.2008.01.006
17	A K Verma, S Rajotia. A review of machining feature recognition methodologies. International Journal of Computer Integrated Manufacturing, 2010, 23(4): 353–368 https://doi.org/10.1080/09511921003642121
18	L Hanayneh, Y Wang, Y Wang, et al. Feature mapping automation for CAD data exchange. In: Proceedings of ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. NewYork: ASME, 2008, 1257–1266
19	L Fei, G Lu, W Jia, et al. Feature extraction methods for palmprint recognition: A survey and evaluation. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018, 49(2): 346–363 https://doi.org/10.1109/TSMC.2018.2882630
20	S Afshar, T J Hamilton, J Tapson, et al. Investigation of event-based surfaces for high-speed detection, unsupervised feature extraction, and object recognition. Frontiers in Neuroscience, 2019, 12: 1047 https://doi.org/10.3389/fnins.2018.01047
21	L Lu, S Zhao. High-quality point sampling for B-spline fitting of parametric curves with feature recognition. Journal of Computational and Applied Mathematics, 2019, 345: 286–294 https://doi.org/10.1016/j.cam.2018.04.008
22	W Wang, Y Li, L Tang. Drive geometry construction method of machining features for aircraft structural part numerical control machining. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 2014, 228(10): 1214–1225 https://doi.org/10.1177/0954405413516953
23	M Kang, G Kim, K Eum, et al. A classification of multi-axis features based on manufacturing process. International Journal of Precision Engineering and Manufacturing, 2014, 15(6): 1255–1263 https://doi.org/10.1007/s12541-014-0465-z
24	J Yan, W Li. Group technology based feature extraction methodology for data mining. In: Proceedings of the 5th International Conference on Fuzzy Systems and Knowledge Discovery. Shandong: IEEE, 2008, 235–239 https://doi.org/10.1109/FSKD.2008.604
25	H M Xu, D B Li. A clustering-based modeling scheme of the manufacturing resources for process planning. International Journal of Advanced Manufacturing Technology, 2008, 38(1‒2): 154–162 https://doi.org/10.1007/s00170-007-1075-z
26	C Chen, L Wang. Product platform design through clustering analysis and information theoretical approach. International Journal of Production Research, 2008, 46(15): 4259–4284 https://doi.org/10.1080/00207540701199693
27	S Soomro, A Munir, K N Choi. Fuzzy c-means clustering based active contour model driven by edge scaled region information. Expert Systems with Applications, 2019, 120: 387–396 https://doi.org/10.1016/j.eswa.2018.10.052
28	M Riani, A C Atkinson, A Cerioli, et al. Efficient robust methods via monitoring for clustering and multivariate data analysis. Pattern Recognition, 2019, 88: 246–260 https://doi.org/10.1016/j.patcog.2018.11.016
29	Y Sato, K Izui, T Yamada, et al. Data mining based on clustering and association rule analysis for knowledge discovery in multiobjective topology optimization. Expert Systems with Applications, 2019, 119: 247–261 https://doi.org/10.1016/j.eswa.2018.10.047
30	S K Moon, D A McAdams. A design method for developing a universal product family in a dynamic market environment. In: Proceedings of ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. San Diego: ASME, 2009, 941–950
31	R E H Khalaf, B Agard, B Penz. Simultaneous design of a product family and its related supply chain using a Tabu Search algorithm. International Journal of Production Research, 2011, 49(19): 5637–5656 https://doi.org/10.1080/00207543.2010.519737
32	E Ostrosi, A J Fougères, M Ferney, et al. A fuzzy configuration multi-agent approach for product family modelling in conceptual design. Journal of Intelligent Manufacturing, 2012, 23(6): 2565–2586 https://doi.org/10.1007/s10845-011-0541-5
33	Y Zhao, M Zhang, N Su, et al. Product family extension configuration design: The theory and method. In: Proceedings of 2010 the 2nd International Conference on Computer and Automation Engineering (ICCAE). Singapore: IEEE, 2010, 321–326
34	X G Ming, J Q Yan, W F Lu, et al. Mass production of tooling product families via modular feature based design to manufacturing collaboration in PLM. Journal of Intelligent Manufacturing, 2007, 18(1): 185–195 https://doi.org/10.1007/s10845-007-0012-1
35	Q Wang, D Tang, S Li, et al. An optimization approach for the coordinated low-carbon design of product family and remanufactured products. Sustainability, 2019, 11(2): 460 https://doi.org/10.3390/su11020460
36	Q Song, Y Ni. Optimal platform design with modularity strategy under fuzzy environment. Soft Computing, 2019, 23(3): 1059–1070
37	J Wang, Y He, H Tian, et al. Retrieving 3D CAD model by freehand sketches for design reuse. Advanced Engineering Informatics, 2008, 22(3): 385–392 https://doi.org/10.1016/j.aei.2008.04.001
38	M Li, Y F Zhang, J Y H Fuh, et al. Toward effective mechanical design reuse: CAD model retrieval based on general and partial shapes. Journal of Mechanical Design, 2009, 131(12): 124501 https://doi.org/10.1115/1.4000253
39	F Bosche, C T Haas. Automated retrieval of 3D CAD model objects in construction range images. Automation in Construction, 2008, 17(4): 499–512 https://doi.org/10.1016/j.autcon.2007.09.001
40	A Godil. Applications of 3D shape analysis and retrieval. In: Proceedings of 2009 IEEE Applied Imagery Pattern Recognition Workshop. Washington, D.C.: IEEE, 2009 https://doi.org/10.1109/AIPR.2009.5466293
41	J Xie, G Dai, Y Fang. Deep multimetric learning for shape-based 3D model retrieval. IEEE Transactions on Multimedia, 2017, 19(11): 2463–2474 https://doi.org/10.1109/TMM.2017.2698200
42	H Haj Mohamed, S Belaid, W Naanaa, et al. Local commute-time guided MDS for 3D non-rigid object retrieval. Applied Intelligence, 2018, 48(9): 2873–2883 https://doi.org/10.1007/s10489-017-1114-x
43	L Wang. Integration of CAD and boundary element analysis through subdivision methods. Computers & Industrial Engineering, 2009, 57(3): 691–698 https://doi.org/10.1016/j.cie.2009.01.009
44	S L Sing, F E Wiria, W Y Yeong. Selective laser melting of lattice structures: A statistical approach to manufacturability and mechanical behavior. Robotics and Computer-integrated Manufacturing, 2018, 49: 170–180 https://doi.org/10.1016/j.rcim.2017.06.006

[1]	TAI Ligang, LI Dianqi, ZHONG Tingxiu, LI Zhi. Research on integrating customization design for mechanical product[J]. Front. Mech. Eng., 2007, 2(1): 89-93.

Viewed

Full text

Abstract

Cited

Shared

Discussed